def get_matrix(self):
mlist = []
for m in range(self.size):
rlist = []
for n in range(self.size):
rlist.append(nw.complex(self[m][n]))
mlist.append(rlist)
return nw.matrix(mlist)
def _calculate_coefficients(enes, smatdata, asymcalc):
num_data = len(smatdata)
num_poly_terms = num_data / 2
num_coeffs = num_poly_terms + 1
num_channels = nw.shape(smatdata[enes[0]])[0]
alphas = _initialise_coefficients(num_coeffs, num_channels)
betas = _initialise_coefficients(num_coeffs, num_channels)
for j in range(num_channels):
sys_mat = nw.matrix(
_get_sys_mat_init(num_data, num_channels, num_poly_terms))
res_vec = nw.matrix(_get_res_vec_init(num_data, num_channels))
for i in range(num_channels):
ei = 0
for ene in enes:
for ti in range(
num_poly_terms
): #We have two indices ci (coefficient) and ti (term). We know the first term in the poly expansion so num_coeffs = num_poly_terms + 1
exp = ti + 1
for k in range(num_channels):
if k == i:
alpha_coeff = _primary_alpha(
smatdata, asymcalc, i, j, ene, exp)
beta_coeff = _primary_beta(smatdata, asymcalc, i,
j, ene, exp)
else:
alpha_coeff = _secondary_alpha(
smatdata, asymcalc, i, j, k, ene, exp)
beta_coeff = _secondary_beta(
smatdata, asymcalc, i, j, k, ene, exp)
sys_mat[
_row(num_data, i, ei),
_alpha_index(num_poly_terms, k, ti)] = alpha_coeff
sys_mat[_row(num_data, i, ei),
_beta_index(num_poly_terms, num_channels, k, ti
)] = beta_coeff
res_vec[_row(num_data, i, ei),
0] = _result(smatdata, i, j, ene)
ei += 1
coeff_vec = nw.lin_solve(sys_mat, res_vec)
_copy_column_coeffs(alphas, betas, coeff_vec, num_poly_terms,
num_channels, num_coeffs, j)
return alphas, betas
def gradient(self):
keys = self.sorted_keys()
valshape = self.valshape()
dVec_diffs = []
for i in range(valshape[0]):
dVec_diffs.append(self.create_reduced_dim(i).gradient())
new_item = self._get_dMat()
self._set_chart_title_for_new(new_item, " gradient")
for key in keys:
new_item[key] = nw.matrix([[0.+0.j]*valshape[1]]*valshape[0])
for i in range(valshape[0]):
for j in range(valshape[1]):
new_item[key][i,j] = dVec_diffs[i][key][j]
return new_item
import pynumwrap as nw
nw.use_python_types()
#Inelastic data for two channel radial well a=1.0 v1=2.0 v2=2.0 th1=0.0 th2=2.0 lambda=1.0.
smatdata_inel = {
(1.0 + 0j):
nw.matrix(
[[-0.903506179588 + 0.428575061624j, 1.351949645563 + 0.855044226986j],
[1.351949645563 + 0.855044226986j,
-10.729076216556 - 1.27943423712j]]),
(2.4 + 0j):
nw.matrix([[
-0.015142011020 + 0.936235906011j, -0.214050508716 - 0.278236280014j
], [-0.214050508716 - 0.278236280014j,
-0.908819049951 - 0.225421575385j]]),
(3.8 + 0j):
nw.matrix([[
0.359311942131 + 0.900883718856j, -0.243477511566 + 0.004706878441j
], [-0.243477511566 + 0.004706878441j,
-0.324224934713 + 0.914097772871j]]),
(5.2 + 0j):
nw.matrix(
[[0.536533824095 + 0.817428091005j, -0.192416562985 + 0.083178350109j],
[-0.192416562985 + 0.083178350109j,
0.227866378764 + 0.950859685648j]]),
(6.6 + 0j):
nw.matrix(
[[0.595880541354 + 0.776177020474j, -0.171324245796 + 0.114558352498j],
[-0.171324245796 + 0.114558352498j,
0.489628987760 + 0.84722360745j]]),
def cal_pos_neg_imag_sub_mat(self, i):
return nw.matrix([[float(i), 2 * float(i)],
[10 + float(i), 10 + 2 * float(i)]])
def _initialise_coefficients(num_coeffs, num_channels):
coeffs = []
for _ in range(0, num_coeffs):
mat = nw.matrix(_get_zero_list_mats(num_channels))
coeffs.append(mat)
return coeffs
def finialise(self):
self.s_calc.finialise()
self.mat_fin_p = nw.matrix(self.list_fin_p)
self.mat_fout_p = nw.matrix(self.list_fout_p)
def finialise(self):
self.mat_fin = nw.matrix(self.list_fin)
self.mat_fout = nw.matrix(self.list_fout)
self.mat_fin_inv = nw.invert(self.mat_fin)
import pynumwrap as nw
TESTDPS = 40
nw.use_mpmath_types(TESTDPS)
smatdata_inel = {
nw.mpf('1.0'):
nw.matrix([[
nw.mpc(real='-0.9035061795881897271183573871099408926215',
imag='0.4285750616239281985579964961941083103659'),
nw.mpc(real='1.35194964556261028295718440465645224833',
imag='0.855044226986120550015062446068837955507')
],
[
nw.mpc(real='1.35194964556261028295718440465645224833',
imag='0.855044226986120550015062446068837955507'),
nw.mpc(real='-10.72907621655570403075417982784331447184',
imag='-1.279434237119580005892120398441009444365')
]]),
nw.mpf('2.4'):
nw.matrix([[
nw.mpc(real='-0.01514201102047645349961872516635962963436',
imag='0.9362359060112529631344928051470581148755'),
nw.mpc(real='-0.2140505087155649231708634185540283542351',
imag='-0.2782362800142209028684770156904336133757')
],
[
nw.mpc(real='-0.2140505087155649231708634185540283542351',
imag='-0.2782362800142209028684770156904336133757'),
nw.mpc(real='-0.9088190499507801981491861480368798369446',
imag='-0.2254215753849156510585342449105225513031')
]]),